Recommender Systems and Their Advanced Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 18571

Special Issue Editors


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Guest Editor
The Data Science Institute, University of Technology Sydney, Sydney, NSW, Australia
Interests: data science; machine learning; recommender system
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
DeepBlue Academic of Sciences, Shanghai 200336, China
Interests: recommendation systems; machine learning; data science; time series analysis

Special Issue Information

Dear Colleagues,

Today, the renaissance of artificial intelligence (AI) has attracted huge attention in everyday real life. Recommender systems, as one of the most popular applications of AI, have already become an indispensable means for helping web users to identify the most relevant information/services in the era of information overload. The applications of such systems are multifaceted, including targeted advertising, intelligent financial assistant, and e-commerce, and are bringing immense convenience to people’s daily lives.

This Special Issue solicits the latest and most significant contributions on developing and applying advanced recommender systems. Any novel works on recommender systems and/or their innovative applications are welcome.

Relevant topic areas

This Special Issue invites submissions on all topics of algorithms and theories for recommender systems, including but not limited to:

  • Deep neural models for recommender systems
  • Shallow neural models for recommender systems
  • Neural theories, particularly for recommender systems
  • Theoretical analysis of neural models for recommender systems
  • Theoretical analysis for recommender systems
  • Data characteristics and complexity analysis in recommender systems
  • Non-IID (non-independent and identical distribution) theories and practices for recommender systems
  • Auto-ML for recommender systems
  • Privacy issues in recommender systems
  • Recommendations on small data sets
  • Complex behavior modeling and analysis for recommender systems
  • Psychology-driven user modeling for recommender systems
  • Brain-inspired neural models for recommender systems
  • Explainable recommender systems
  • Adversarial recommender systems
  • Multimodal recommender systems
  • Rich-context recommender systems
  • Heterogeneous relation modeling in recommender systems
  • Visualization in recommender systems
  • New evaluation metrics and methods for recommender systems
  • Case study of recommender systems in real-world applications

Dr. Shoujin Wang
Dr. Qi Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • recommender systems
  • recommendations
  • user modeling
  • machine learning
  • deep learning

Published Papers (12 papers)

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Research

19 pages, 5038 KiB  
Article
R-PreNet: Deraining Network Based on Image Background Prior
by Congyu Jiao, Fanjie Meng, Tingxuan Li and Ying Cao
Appl. Sci. 2023, 13(21), 11970; https://doi.org/10.3390/app132111970 - 2 Nov 2023
Cited by 1 | Viewed by 729
Abstract
Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Although many CNN-based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the [...] Read more.
Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Although many CNN-based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, a novel network architecture, R-PReNet, is introduced for single image denoising with an emphasis on preserving the background structure. The framework effectively exploits the cyclic recursive structure inherent in PReNet. Additionally, the residual channel prior (RCP) and feature fusion modules have been incorporated, enhancing denoising performance by emphasizing background feature information. Compared with the previous methods, this approach offers notable improvement in rainstorm images by reducing artifacts and restoring visual details. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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15 pages, 3120 KiB  
Article
CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation
by Ming He, Boyang An, Jiwen Wang and Hao Wen
Appl. Sci. 2023, 13(20), 11176; https://doi.org/10.3390/app132011176 - 11 Oct 2023
Viewed by 946
Abstract
Providing interpretable explanations can notably enhance users’ confidence and satisfaction with regard to recommender systems. Counterfactual explanations demonstrate remarkable performance in the realm of explainable sequential recommendation. However, current counterfactual explanation models designed for sequential recommendation overlook the temporal dependencies in a user’s [...] Read more.
Providing interpretable explanations can notably enhance users’ confidence and satisfaction with regard to recommender systems. Counterfactual explanations demonstrate remarkable performance in the realm of explainable sequential recommendation. However, current counterfactual explanation models designed for sequential recommendation overlook the temporal dependencies in a user’s past behavior sequence. Furthermore, counterfactual histories should be as similar to the real history as possible to avoid conflicting with the user’s genuine behavioral preferences. This paper presents counterfactual explanations by Considering temporal dependencies (CETD), a counterfactual explanation model that utilizes a variational autoencoder (VAE) for sequential recommendation and takes into account temporal dependencies. To improve explainability, CETD employs a recurrent neural network (RNN) when generating counterfactual histories, thereby capturing both the user’s long-term preferences and short-term behavior in their real behavioral history. Meanwhile, CETD fits the distribution of reconstructed data (i.e., the counterfactual sequences generated by VAE perturbation) in a latent space, and leverages learned variance to decrease the proximity of counterfactual histories by minimizing the distance between the counterfactual sequences and the original sequence. Thorough experiments conducted on two real-world datasets demonstrate that the proposed CETD consistently surpasses current state-of-the-art methods. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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16 pages, 2201 KiB  
Article
A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition
by Yu Mao, Yuxuan Cheng and Chunyu Shi
Appl. Sci. 2023, 13(16), 9464; https://doi.org/10.3390/app13169464 - 21 Aug 2023
Cited by 1 | Viewed by 794
Abstract
In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, [...] Read more.
In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users’ attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of “job seeker user-position-attention layered”. Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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12 pages, 648 KiB  
Article
A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks
by Ran Li, Yuexin Li, Jingsheng Lei and Shengying Yang
Appl. Sci. 2023, 13(16), 9315; https://doi.org/10.3390/app13169315 - 16 Aug 2023
Viewed by 999
Abstract
Most existing recommendation models only consider single user–item interaction information, which leads to serious cold-start or data sparsity problems. In practical applications, a user’s behavior is multi-type, and different types of user behavior show different semantic information. To achieve more accurate recommendations, a [...] Read more.
Most existing recommendation models only consider single user–item interaction information, which leads to serious cold-start or data sparsity problems. In practical applications, a user’s behavior is multi-type, and different types of user behavior show different semantic information. To achieve more accurate recommendations, a major challenge comes from being able to handle heterogeneous behavior data from users more finely. To address this problem, this paper proposes a multi-behavior recommendation framework based on a graph neural network, which captures personalized semantics of specific behavior and thus distinguishes the importance of different behaviors for predicting the target behavior. Meanwhile, this model establishes dependency relationships among different types of interaction behaviors under the graph-based information transfer network, and the graph convolutional network is further used to capture the high-order complexity of interaction graphs. The experimental results of three benchmark datasets show that the proposed graph-based multi-behavior recommendation model displays significant improvements in recommendation accuracy compared to the baseline method. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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21 pages, 3709 KiB  
Article
MDAR: A Knowledge-Graph-Enhanced Multi-Task Recommendation System Based on a DeepAFM and a Relation-Fused Multi-Gead Graph Attention Network
by Songjiang Li, Qingxia Xue and Peng Wang
Appl. Sci. 2023, 13(15), 8697; https://doi.org/10.3390/app13158697 - 27 Jul 2023
Viewed by 1178
Abstract
In recent years, MKR has attracted increasing attention due to its ability to enhance the accuracy of recommendation systems through cooperation between the RS tasks and the KGE tasks, allowing for complementarity of the information. However, there are still three challenging issues: historical [...] Read more.
In recent years, MKR has attracted increasing attention due to its ability to enhance the accuracy of recommendation systems through cooperation between the RS tasks and the KGE tasks, allowing for complementarity of the information. However, there are still three challenging issues: historical behavior preferences, missing data, and knowledge graph completion. To tackle these challenging problems, we propose MDAR, a multi-task learning approach that combines DeepFM with an attention mechanism (DeepAFM) and a relation-fused multi-head graph attention network (RMGAT). Firstly, we propose to leverage the attention mechanism in the DeepAFM to distinguish the importance of different features for target prediction by assigning different weights to different interaction features of the user and the item, which solves the first problem. Secondly, we introduce deep neural networks (DNNs) to extract the deep semantic information in the cross-compressed units by obtaining the high-dimensional features of the interactions between the RS task and the KG task to solve the second problem. Lastly, we design a multi-head graph attention network for relationship fusion (RMGAT) in the KGE task, which learns entity representations through the different contributions of the neighbors by aggregating the relationships into the attention network of the knowledge graph and by obtaining information about the neighbors with different importance for different relationships, effectively solving the third problem. Through experimenting on real-world public datasets, we demonstrate that MDAR obtained substantial results over state-of-the-art baselines for recommendations for movie, book, and music datasets. Our results underscore the effectiveness of MDAR and its potential to advance recommendation systems in various domains. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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21 pages, 3964 KiB  
Article
Multilabel Genre Prediction Using Deep-Learning Frameworks
by Fatima Zehra Unal, Mehmet Serdar Guzel, Erkan Bostanci, Koray Acici and Tunc Asuroglu
Appl. Sci. 2023, 13(15), 8665; https://doi.org/10.3390/app13158665 - 27 Jul 2023
Cited by 2 | Viewed by 2415
Abstract
In this study, transfer learning has been used to overcome multilabel classification tasks. As a case study, movie genre classification by using posters has been chosen. Six state-of-the-art pretrained models, VGG16, ResNet, DenseNet, Inception, MobileNet, and ConvNeXt, have been employed for this experiment. [...] Read more.
In this study, transfer learning has been used to overcome multilabel classification tasks. As a case study, movie genre classification by using posters has been chosen. Six state-of-the-art pretrained models, VGG16, ResNet, DenseNet, Inception, MobileNet, and ConvNeXt, have been employed for this experiment. The movie posters have been obtained from Internet Movie Database (IMDB). The dataset has been divided using an iterative stratification technique. A sequence of dense layers has been added on top of each model and these models have been trained and fine-tuned. All the results of the models compared considered accuracy, loss, Hamming loss, F1-score, precision, and AUC metrics. When the metrics used were evaluated, the most successful result regarding accuracy has been obtained from the modified DenseNet architecture at 90%. Also, the ConvNeXt, which is the newest model among all, performed quite satisfactorily, reaching over 90% accuracy. This study uses an iterative stratification method to split an unbalanced dataset which provides more reliable results than the classical splitting method which is the common method in the literature. Also, the feature extraction capabilities of the six pretrained models have been compared. The outcome of this study shows promising results regarding multilabel classification. As for future work, it is planned to enhance this study by using natural language processing and ensemble methods. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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18 pages, 646 KiB  
Article
Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
by Longxiang Shi, Qi Zhang, Shoujin Wang, Zilin Zhang, Binbin Zhou, Minghui Wu and Shijian Li
Appl. Sci. 2023, 13(13), 7726; https://doi.org/10.3390/app13137726 - 29 Jun 2023
Cited by 1 | Viewed by 883
Abstract
Nowadays, interactive recommendation systems (IRS) play a significant role in our daily life. Recently, reinforcement learning has shown great potential in solving challenging tasks in IRS, since it can focus on long-term profit and can capture the dynamic preference of users. However, existing [...] Read more.
Nowadays, interactive recommendation systems (IRS) play a significant role in our daily life. Recently, reinforcement learning has shown great potential in solving challenging tasks in IRS, since it can focus on long-term profit and can capture the dynamic preference of users. However, existing RL methods for IRS have two typical deficiencies. First, most state representation models use left-to-right recurrent neural networks to capture the user dynamics, which usually fail to handle the long and noisy sequential data in real life. Second, an IRS always needs to handle millions of items, leading to a large discrete action space in RL settings, which has not been fully addressed by the inefficient existing works. To bridge these deficiencies, in this paper, we propose attention-based tree recommendation (ATRec), an efficient tree-structured policy with attention-based state representation for IRS. ATRec uses an attention-based state representation model to effectively capture the user’s dynamic preference hidden in the long and noisy sequence of behaviors. Moreover, to improve the learning efficiency, we propose an efficient tree-structured policy representation method, in which a complete tree is devised to represent the policy, and a novel parameter-sharing strategy is introduced. Extensive experiments are conducted on three real-world datasets and the results show the proposed ATRec obtains 42.3% improvement over some of the state of the arts methods in the hit rate and 21.4% improvement in the mean reciprocal rank of the top 30 ranked items. Additionally, the learning and decision efficiency can also be improved at an average of 35.5%. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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17 pages, 3822 KiB  
Article
Improving Quality of Life in Chronic Patients: A Pilot Study on the Effectiveness of a Health Recommender System and Its Usability
by Alberto del Rio, Jennifer Jimenez, Rodrigo Medina-García, Cristina Lozano-Hernández, Federico Alvarez and Javier Serrano
Appl. Sci. 2023, 13(10), 5850; https://doi.org/10.3390/app13105850 - 9 May 2023
Cited by 2 | Viewed by 1233
Abstract
The TeNDER project aims to improve the quality of life (QoL) of chronic patients through an integrated care ecosystem. This study evaluates the health recommender system (HRS) developed for the project, which offers personalized recommendations based on data collected from a set of [...] Read more.
The TeNDER project aims to improve the quality of life (QoL) of chronic patients through an integrated care ecosystem. This study evaluates the health recommender system (HRS) developed for the project, which offers personalized recommendations based on data collected from a set of monitoring devices. The list of notifications covered different areas of daily life such as physical activity, nutrition, and sleep. We conducted this case study to evaluate the effectiveness and usability of the HRS in providing accurate and relevant recommendations to users. Evaluation process consisted on survey administration for QoL assessment and the satisfaction and usability of the HRS. The four-week pilot study involved several patients and caregivers and demonstrated that the HRS was perceived as user-friendly, consistent, and helpful, with a positive impact on patients’ QoL. However, the study highlights the need for improvement in terms of personalization of recommendations. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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14 pages, 2943 KiB  
Article
Personalized Privacy Protection-Preserving Collaborative Filtering Algorithm for Recommendation Systems
by Bin Cheng, Ping Chen, Xin Zhang, Keyu Fang, Xiaoli Qin and Wei Liu
Appl. Sci. 2023, 13(7), 4600; https://doi.org/10.3390/app13074600 - 5 Apr 2023
Cited by 4 | Viewed by 1755
Abstract
With the rapid development of ubiquitous data collection and data analysis, data privacy in a recommended system is facing more and more challenges. Differential privacy technology can provide strict privacy protection while reducing the risk of privacy leakage, but it also introduces unwanted [...] Read more.
With the rapid development of ubiquitous data collection and data analysis, data privacy in a recommended system is facing more and more challenges. Differential privacy technology can provide strict privacy protection while reducing the risk of privacy leakage, but it also introduces unwanted noise, which makes the performance of the recommender system worsen. Among different users, the degree of their sensitivity to privacy is usually different. Thus, through considering the impact of users’ personalized requirements, the collaborative filtering algorithm can be designed to reduce the amount of unwanted noise. Taking the above assertions into account, we propose a collaborative filtering algorithm based on personalized privacy protection. First, it locally classifies ratings by privacy sensitivity on the user side, then utilizes the random flip mechanism to protect the privacy-sensitive ratings. Then, after the server catches the perturbed rating data, we reconstruct the joint item-item distribution through the Bayesian estimation method. Experimental results show that our proposed algorithm can significantly improve the recommendation performance of recommendation systems while protecting users’ privacy. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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20 pages, 11350 KiB  
Article
Body Shape-Aware Object-Level Outfit Completion for Full-Body Portrait Images
by Xiaoya Chong and Howard Leung
Appl. Sci. 2023, 13(5), 3214; https://doi.org/10.3390/app13053214 - 2 Mar 2023
Cited by 1 | Viewed by 2348
Abstract
Modeling fashion compatibility between different categories of items and forming personalized outfits have become important topics in recommender systems recently. However, item compatibility and outfit recommendation have been explored in perfect settings in the past, where high-quality images of items from the front [...] Read more.
Modeling fashion compatibility between different categories of items and forming personalized outfits have become important topics in recommender systems recently. However, item compatibility and outfit recommendation have been explored in perfect settings in the past, where high-quality images of items from the front view or user profiles are available. In this paper, we propose a new task called Complete The full-body Portrait (CTP) for real-world fashion images (e.g., street photos and selfies), which is able to recommend the most compatible item for a masked scene where the outfit is incomplete. Visual compatibility and personalization are the key points for accurate scene-based recommendations. In our approach, the former is accomplished by calculating the visual distance of the query scene and target item in latent space, while the latter is achieved by taking the body-shape information of the human subject into consideration. To obtain side information to train our model, ResNet-50, YOLOv3 and SMPLify-X models are adopted to extract visual features, detect item objects, and reconstruct a 3D body mesh, respectively. Our approach first predicts the missing item category from the masked scene, and then finds the most compatible items from the predicted category through computing visual distances at image level, region level and object level, together with measuring human body-shape compatibility. We conduct extensive experiments on two real-world datasets, Street2Shop and STL-Fashion. Both quantitative and qualitative results show that our model outperforms all baseline models. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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19 pages, 964 KiB  
Article
Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives
by Jiayun Wang, Biligsaikhan Batjargal, Akira Maeda, Kyoji Kawagoe and Ryo Akama
Appl. Sci. 2023, 13(4), 2435; https://doi.org/10.3390/app13042435 - 14 Feb 2023
Cited by 1 | Viewed by 1381
Abstract
Digital archives (DAs) usually store diverse expert-level materials. Nowadays, access to DAs is increasing for non-expert users, However, they might have difficulties formulating appropriate search queries to find the necessary information. In response to this problem, we propose a query log-based query recommendation [...] Read more.
Digital archives (DAs) usually store diverse expert-level materials. Nowadays, access to DAs is increasing for non-expert users, However, they might have difficulties formulating appropriate search queries to find the necessary information. In response to this problem, we propose a query log-based query recommendation algorithm that provides expert knowledge to non-expert users, thus supporting their information seeking in DAs. The use case considered is one where after users enter some general queries, they will be recommended semantically similar expert-level queries in the query logs. The proposed modified conditional restricted Boltzmann machines (M-CRBMs) are capable of utilizing the rich metadata in DAs, thereby alleviating the sparsity problem that conventional restricted Boltzmann machines (RBMs) will face. Additionally, compared with other CRBM models, we drop a large number of model weights. In the experiments, the M-CRBMs outperform the conventional RBMs when using appropriate metadata, and we find that the recommendation results are relevant to the metadata fields that are used in M-CRBMs. Through experiments on the Europeana dataset, we also demonstrate the versatility and scalability of our proposed model. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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18 pages, 2739 KiB  
Article
Multi-Level Knowledge-Aware Contrastive Learning Network for Personalized Recipe Recommendation
by Zijian Bai, Yinfeng Huang, Suzhi Zhang, Pu Li, Yuanyuan Chang and Xiang Lin
Appl. Sci. 2022, 12(24), 12863; https://doi.org/10.3390/app122412863 - 14 Dec 2022
Viewed by 1614
Abstract
Personalized recipe recommendation is attracting more and more attention, which can help people make choices from the exploding growth of online food information. Unlike other recommendation tasks, the target of recipe recommendation is a non-atomic item, so attribute information is especially important for [...] Read more.
Personalized recipe recommendation is attracting more and more attention, which can help people make choices from the exploding growth of online food information. Unlike other recommendation tasks, the target of recipe recommendation is a non-atomic item, so attribute information is especially important for the representation of recipes. However, traditional collaborative filtering or content-based recipe recommendation methods tend to focus more on user–recipe interaction information and ignore higher-order semantic and structural information. Recently, graph neural networks (GNNs)-based recommendation methods provided new ideas for recipe recommendation, but there was a problem of sparsity of supervised signals caused by the long-tailed distribution of heterogeneous graph entities. How to construct high-quality representations of users and recipes becomes a new challenge for personalized recipe recommendation. In this paper, we propose a new method, a multi-level knowledge-aware contrastive learning network (MKCLN) for personalized recipe recommendation. Compared with traditional comparative learning, we design a multi-level view to satisfy the requirement of fine-grained representation of users and recipes, and use multiple knowledge-aware aggregation methods for node fusion to finally make recommendations. Specifically, the local-level includes two views, interaction view and semantic view, which mine collaborative information and semantic information for high-quality representation of nodes. The global-level learns node embedding by capturing higher-order structural information and semantic information through a network structure view. Then, a kind of self-supervised cross-view contrastive learning is invoked to make the information of multiple views collaboratively supervise each other to learn fine-grained node embeddings. Finally, the recipes that satisfy personalized preferences are recommended to users by joint training and model prediction functions. In this study, we conduct experiments on two real recipe datasets, and the experimental results demonstrate the effectiveness and advancement of MKCLN. Full article
(This article belongs to the Special Issue Recommender Systems and Their Advanced Application)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration
Author: Li
Highlights: we compare and analyze the performance of the proposed model on MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively.

Title: Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration
Author: Li
Highlights: This paper proposes that the PMF model is based on the hybrid AdaBoost method. FCM is used to calculate the similarity of the rating matrix of the user-item , which effectively solves the improvement of rating accuracy.

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